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All across the world, more and more data is being generated during day-to-day activities. This infographic from OnlineBusinessDegree.org takes a look at several ways this “Big Data” will impact us in the future on a social, political and economic level.

Tell us in the comments below on how you think big data will evolve in the future.

Please Include Attribution to OnlineBusinessDegree.org with this Graphic

Big data is a complex, tricky thing to govern. Often, it’s an unholy siloed mess of disparate databases under various business units, on various data platforms, and managed by various “stewards” with various tools and workflows.

Consolidation of your big-data assets must be an ongoing initiative, both to reduce overhead and to free up the insights that come from correlating disparate data sets. But you can scarcely consolidate such a mission-critical resource without addressing the administrative issue of big-data governance head-on. Presumably, you already have some level of governance–aka data stewardship or master data management–in your data warehousing and business intelligence practices.

Smart big-data consolidation demands the following double-barreled approach to governing the assets that matter:

Governing analytic data: Keeping your big data under control means, among other things, determining what small subset of it should be managed with tight stewardship. Usually, those are the system-of-record relational data you’ve long managed within the master tables of your enterprise data warehouse. In other words, your official records on customer, finances, human resources, the supply chain will still be governed tightly in the era of big data, and probably on your scaled-up enterprise data warehouse. But the larger volume of unstructured data–such as social marketing intelligence, real-time sensor data feeds, browser clickstream sessions, and IT system logs–can remain outside your governance practice until such time as it is linked to systems of record.

Governing analytic models: Big-data applications ride on a never-ending stream of new statistical, predictive, segmentation, behavioral, and other advanced analytic models. As you ramp up your data scientist teams and give them more powerful modeling tools, you will soon be swamped with models. Big data analytics demands governance of analytic models, if they’re to be deployed into production business applications. Key governance features include check in/check-out, change tracking, version control, and collaborative development and validation. Your big-data sandboxing platforms and modeling tools should ensure consistent governance automation, and managed collaboration across multidisciplinary teams working on your most challenging big data analytics initiatives.

No, governance is not the sexy side of big data. It’s often an afterthought in big-data projects. But it’s absolutely essential if you wish to keep your data clean, your models fit, and your big-data applications delivering reliable insights throughout the business.

Last week, Amazon hosted AWS re:Invent, its first global and partner conference. It featured several sessions on different ways to prosper in the AWS cloud including cloud migration best practices and new AWS services. Check out this user video featuring CTO Dr. Werner Vogels and CEO Jeff Bezos during their ‘fireside chat’.

Disclaimer: All videos presented in the “YouTube IT Video of the Week” series are subjectively selected by ITKnowledgeExchange.com community managers and staff for entertainment purposes only. They are not sponsored or influenced by outside sources.

Though many people still head to the big box stores at 3 a.m. on Black Friday, online shopping continues to rise. Online sales were up 20.7 percent over last year; this infographic from IBM shows all topics related to Black Friday including mobile and tablet sales.

We’ll be sharing IT events each month here on the Enterprise IT Watch blog. Got an event to add to our list? Let us know via Twitter (@ITKE) or email. Going to one of these events? Share your takeaways (and photos) with us!

JIRA is a great tool to keep track of issues in a software project, be they requirements or bug-reports. You can create a JIRA issue, attach a description of what you want, and assign it to someone. Or to yourself. The tool generates charts and graphs that will impress your boss.

But there’s a problem I call JIRA-mandering and it’s as bad a thing as wickedly-drawn political districts.

The people who are defining a system under development, or reporting problems in an existing system may not have a clear notion of what they want or what’s wrong. This vagueness isn’t a bad thing because we need to capture issues and give them visibility. It’s better than nothing! But the vagueness can lead to JIRA-mandering as we learn what we want and as one thing leads to another.

Suppose you’ve got a vague requirement to publish something, but when you get into the implementation, you learn of constraints and considerations you were unaware of at the outset. It’s easy to tack these considerations onto the original JIRA issue.

Another possibility is that when you originally formulate a JIRA issue you want X and Y and Z. Only trouble is that at the time you didn’t realize that X and Y are as easy as getting milk and cigarettes from the corner store, while Z is like flying to the moon to get rocks. I can hear my boss saying, “I appreciate the milk and smokes, Steve, but you’re not closing the issue.”

Perhaps you’ve heard of SMART criteria (Specific, Measurable, Attainable, Relevant, and Timely). “Hey, boss, did you realize you were asking for the moon?” Or “Hey boss, what do you really need moonrocks for?”

I think JIRA issues should be as SMART as you know how to make them. And you’ve got to have an understanding with your stakeholders that JIRA issues are subject to change as we learn and work through what the software needs. I propose a continuous process of refining JIRA issues to make them SMARTer.

Whenever someone gives me work, we both want to know when it’ll be done. I know I’m done writing software when it passes an Acceptance Test. (Every test should have one reason to fail, but that’s another story.) Let’s suppose a stakeholder creates a JIRA-mandered issue and assigns it to you. The first thing you should do is determine what the Acceptance Test will be. It’s one of those good habits: start with the end in mind.

When you do this to a JIRA-mandered issue, you’ll discover that either you cannot articulate an Acceptance Test, or you’ll find you’re talking about a collection of tangentially-related tests. Most likely, it’ll be a mix of the two: a fog-ball nestled in amidst a number of better-understood, disparate matters.

If you can’t articulate an Acceptance Test for a JIRA issue, you’ve got to negotiate with your stakeholder. Try to get the issue split into parts that clearly identify the parts you understand and the rest. And I suppose that when you see parts of two different JIRA-mandered issues that naturally belong together, try to recombine them into their own JIRA issue.

Just as gerrymandering undermines the integrity of democratic governance, a haphazard coverage of the requirements and issues in a software system undermines visibility into its development or maintenance.

Steve Poling was born, raised and lives in West Michigan with his wife and kids. He uses his training in Applied Mathematics and Computer Science as a C++/C# poet by day while writing Subversive Fiction by night. Steve has an abiding interest in philosophy and potato cannons. He writes SF, crime fiction, an occasional fractured fairy tale, and steampunk. His current writing project is a steampunk novel, Steamship to Kashmir – provided he isn’t distracted by something new & shiny.

Disclaimer: All videos presented in the “YouTube IT Video of the Week” series are subjectively selected by ITKnowledgeExchange.com community managers and staff for entertainment purposes only. They are not sponsored or influenced by outside sources.

This week, the National Museum of Computing is opening its doors to the world’s oldest computer. Known as ‘The Witch‘, the computer weighs two tons and was used to help scientists crunch large numbers.

After 15 years, the computer is getting restored and this video proves ‘The Witch’ is back in action!

Disclaimer: All videos presented in the “YouTube IT Video of the Week” series are subjectively selected by ITKnowledgeExchange.com community managers and staff for entertainment purposes only. They are not sponsored or influenced by outside sources.

Rather than resist it, organizations should embrace Consumerization to unlock its business potential. This requires a strategic approach, flexible policies and appropriate security and management tools.

The consumerization of IT is the single most influential technology trend of this decade. Companies are already well aware of it, as they wrestle with the growing influence of smartphones, tablets, Facebook, Twitter and on and on. While this growth does bring risks, too many companies make the mistake of trying to resist the influx of consumer IT. So what are the solutions and best practices for a company to turn consumerization into a competitive advantage?

One: Have a plan. Take a strategic approach to Consumerization and develop a cross-organizational plan. IT cannot do this in a vacuum and will have to engage executives, line of business owners (marketing, sales, HR, product development) as well as customers, partners, and internal early adopters. While planning to adopt new consumer technology, IT managers should survey their most innovative users to discover what devices and applications they like and what they find most useful in their work activities. In this way IT will pull from users’ experience rather than pushing IT views to their base.

Two: Say yes – but not to everything for everyone. Develop a set of policies that clearly define what devices and applications are considered corporate-standard (fully supported by IT) vs. tolerated (jointly supported with the user) vs. deprecated (full user liability). In addition, IT should profile the global workforce based on relevant attributes such as role, line of business and location. And then map technologies to user profiles and define SLAs for each intersection.

Three: Put the right infrastructure in place. Deploy appropriate IT tools specifically designed to secure and manage consumer technology in the enterprise. Be aware that while some solutions have already materialized along the lines of specific product segments, no single vendor can provide one single solution covering all functional requirements across all platforms. As vendors enter the Consumerization space with solutions initially developed for adjacent product segments, most solutions tend to offer overlapping core functionality and to lack the cross-platform support critical to protect and manage the full spectrum of Consumer technologies. Therefore, IT will have to integrate multiple offerings across different product categories: security solutions for Internet content security, mobile antimalware and mobile data protection, Mobile Device Management tools for system provisioning and application management, and Telecom Expense Management providers for procurement, support and cost control of voice and data services.

In conclusion, organizations need to embrace consumerization to unlock it business potential. This requires a strategic approach, flexible policies and appropriate security and management tools.

Have you seen this strategy working well in your company? Not at all? Let me know. Leave a comment here.

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